jupyter notebookWant more fancy stuff? Just install RISE!
conda install -c damianavila82 risePlay with your toys!
Here is an option to play with if you can't set up jupyter on your own computer: https://tmpnb.org.
In [1]:
print ('Hello Python!')
In [2]:
x = 3
print (x, type(x))
In [3]:
print (x + 3) # Addition;
print (x - x) # Subtraction;
print (x * 2) # Multiplication;
print (x ** 3) # Exponentiation;
In [4]:
print (x)
x += 1
print (x)
x = x + 1
print (x) # Prints "4"
x *= 2
print (x) # Prints "8"
In [5]:
y = 2.5
print (type(y)) # Prints "<type 'float'>"
print (y, y + 1, y * 2, y ** 2) # Prints "2.5 3.5 5.0 6.25"
Note that unlike many languages, Python does not have unary increment (x++) or decrement (x--) operators.
Python also has built-in types for long integers and complex numbers; you can find all of the details in the documentation.
In [6]:
print (17 / 3) # return float
print (17 // 3) # return integer
print (17 % 3) # Modulo operation
In [7]:
t, f = True, False # Note the Captilzation!
print (type(t)) # Prints "<type 'bool'>"
Now we let's look at the operations:
In [8]:
print (t and f) # Logical AND;
print (t or f) # Logical OR;
print (not t) # Logical NOT;
print (t != f) # Logical XOR;
In [9]:
hello = 'hello' # String literals can use single quotes
world = "world" # or double quotes; it does not matter.
print (hello, len(hello))
In [10]:
hw = hello + ' ' + world # String concatenation
print (hw) # prints "hello world"
In [11]:
# sprintf style string formatting
hw12 = '%s %s %d' % (hello, world, 12)
# Recommended formatting style for Py3.0+ (https://pyformat.info)
new_py3_hw12 = '{:>15} {:1.1f} {}'.format('hello' + ' ' + 'world', 1, 2)
print (hw12)
print (new_py3_hw12)
In [2]:
s = "hello"
print (s.capitalize()) # Capitalize a string; prints "Hello"
print (s.upper()) # Convert a string to uppercase; prints "HELLO"
print (s.rjust(7)) # Right-justify a string, padding with spaces; prints " hello"
print (s.center(7)) # Center a string, padding with spaces; prints " hello "
print (s.replace('ll', '(ell)')) # Replace all instances of one substring with another;
# prints "he(ell)(ell)o"
print (' world '.strip()) # Strip leading and trailing whitespace; prints "world"
In [13]:
"You can type ' inside"
Out[13]:
In [3]:
'You can type \' inside'
Out[3]:
You can find a list of all string methods in the document.
In [5]:
x = [1, 2, 3, 'a', 'b', 'c'] + ['hello'] # list append with the + operator
print (x, x[2]) # access by index
print (x[0]) # index can be negative
In [16]:
x.append('element')
print (x)
print (x.pop(), x)
In [7]:
x = [1, 2, 3, 4, 5]
print (x[2:])
print (x[:3])
print (x[2:5])
x[0:3] = ['a', 'b', 'c'] # modify elements in list
print (x)
In [8]:
y = x[:] # copy list
y[2] = 100 # x won't change
print ('y:', y)
print ('x:', x)
As usual, you can find all the gory details about lists in the documentation.
In [19]:
animals = ['cat', 'dog', 'monkey']
for animal in animals:
print (animal)
If you want access to the index of each element within the body of a loop, use the built-in enumerate function:
In [9]:
animals = ['cat', 'dog', 'monkey']
print (enumerate(animals))
for idx, animal in enumerate(animals):
print ('#%d: %s' % (idx + 1, animal))
In [21]:
nums = [0, 1, 2, 3, 4]
squares = []
for x in nums:
squares.append(x ** 2)
print (squares)
You can make this code simpler using a list comprehension:
In [22]:
nums = [0, 1, 2, 3, 4]
squares = [x ** 2 for x in nums]
print (squares)
List comprehensions can also contain conditions:
In [23]:
nums = [0, 1, 2, 3, 4]
even_squares = [x ** 2 for x in nums if x % 2 == 0]
even_squares_alt = [i ** 2 for i in filter(lambda k: k % 2 == 0 , nums)]
print (even_squares_alt)
In [24]:
nums = [0, 1, 2, 3, 4]
even_squares_or_one = [x ** 2 if x % 2 == 0 else 1 for x in nums]
print (even_squares_or_one)
In [25]:
d = {'cat': 'cute', 'dog': 'furry'} # Create a new dictionary with some data
print (d['cat']) # Get an entry from a dictionary; prints "cute"
print ('cute' in d) # Check if a dictionary has a given key; prints "True"
In [26]:
d['fish'] = 'wet' # Set an entry in a dictionary
print (d['fish']) # Prints "wet"
In [27]:
print (d['monkey']) # KeyError: 'monkey' not a key of d
In [28]:
print (d.get('monkey', 'N/A')) # Get an element with a default; prints "N/A"
print (d.get('fish', 'N/A')) # Get an element with a default; prints "wet"
In [29]:
del d['fish'] # Remove an element from a dictionary
print (d.get('fish', 'N/A')) # "fish" is no longer a key; prints "N/A"
You can find all you need to know about dictionaries in the documentation.
It is easy to iterate over the keys in a dictionary:
In [30]:
d = {'person': 2, 'cat': 4, 'spider': 8}
for animal in d:
legs = d[animal]
print ('A %s has %d legs' % (animal, legs))
If you want access to keys and their corresponding values, use the items method:
In [31]:
d = {'person': 2, 'cat': 4, 'spider': 8}
for animal, legs in d.items():
print ('A %s has %d legs' % (animal, legs))
Dictionary comprehensions: These are similar to list comprehensions, but allow you to easily construct dictionaries. For example:
In [32]:
nums = [0, 1, 2, 3, 4]
even_num_to_square = {x: x ** 2 for x in nums if x % 2 == 0}
print (even_num_to_square)
In [33]:
# Make a dictionary from two lists using zip
l1 = ['EECS445', 'EECS545']
l2 = ['Undergraduate ML', 'Graduate ML']
d = dict(zip(l1, l2))
print (d)
# Unroll dictionary into two tuples
k, v = list(d.keys()), list(d.values())
print (d.items())
print (k, v)
In [34]:
animals = {'cat', 'dog'}
print ('cat' in animals) # Check if an element is in a set; prints "True"
print ('fish' in animals) # prints "False"
In [35]:
animals.add('fish') # Add an element to a set
print ('fish' in animals)
print (len(animals)) # Number of elements in a set;
In [36]:
animals.add('cat') # Adding an element that is already in the set does nothing
print (len(animals))
animals.remove('cat') # Remove an element from a set
print (len(animals))
Loops: Iterating over a set has the same syntax as iterating over a list; however since sets are unordered, you cannot make assumptions about the order in which you visit the elements of the set:
In [37]:
animals = {'dog', 'fish', 'cat'}
for idx, animal in enumerate(animals):
print ('#%d: %s' % (idx + 1, animal))
# Prints "#1: fish", "#2: dog", "#3: cat"
Set comprehensions: Like lists and dictionaries, we can easily construct sets using set comprehensions:
In [38]:
from math import sqrt
print ({int(sqrt(x)) for x in range(30)})
In [39]:
d = {(x, x + 1): x for x in range(0, 10, 2)} # Create a dictionary with tuple keys, note that range can use step args.
t = (0, 1) # Create a tuple
print (type(t))
print (d[t])
print (d[(2, 3)])
In [40]:
t[0] = 1
In [41]:
def get_GPA(x):
if x >= 90:
return "A"
elif x >= 75:
return "B"
elif x >=60:
return "C"
else:
return "F"
for x in [59, 70, 91]:
print (get_GPA(x))
We will often define functions to take optional keyword arguments, like this:
In [42]:
def fib(n = 10):
a = 0
b = 1
while b < n:
print(b, end=',')
a, b = b, a + b
fib()
In [43]:
class Greeter:
# Constructor
def __init__(self, name):
self.name = name # Create an instance variable
# Instance method
def greet(self, loud=False):
if loud:
print ('HELLO, %s!' % self.name.upper())
else:
print ('Hello, %s' % self.name)
g = Greeter('Fred') # Construct an instance of the Greeter class
g.greet() # Call an instance method; prints "Hello, Fred"
g.greet(loud=True) # Call an instance method; prints "HELLO, FRED!"
In [44]:
from modules import fibo
from modules.fibo import fib2
print (fib2(10))
print (fibo.fib2(10))
To use Numpy, we first need to import the numpy package:
In [11]:
import numpy as np
In [46]:
a = np.array([1, 2, 3])
print(a)
print(a.shape)
print(a.dtype)
In [47]:
b = np.array([[0, 2, 4], [1, 3, 5]], dtype = np.float64)
print(b)
print(b.shape)
print(b.dtype)
Numpy also provides many functions to create arrays:
In [48]:
np.zeros(5) # Create an array of all zeros
Out[48]:
In [49]:
np.ones(shape=(3, 4), dtype = np.int32) # Create an array of all ones
Out[49]:
In [50]:
np.full((2,2), 7, dtype = np.int32) # Create a constant array
Out[50]:
In [51]:
np.eye(2) # Create a 2x2 identity matrix
Out[51]:
In [52]:
np.random.random((2,2)) # Create an array filled with random values
Out[52]:
In [53]:
# Create the following rank 2 array with shape (3, 4)
# [[ 1 2 3 4]
# [ 5 6 7 8]
# [ 9 10 11 12]]
a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])
# Use slicing to pull out the subarray consisting of the first 2 rows
# and columns 1 and 2; b is the following array of shape (2, 2):
# [[2 3]
# [6 7]]
b = a[:2, 1:3]
print (b)
In [54]:
print (a[0, 1])
b[0, 0] = 77 # b[0, 0] is the same piece of data as a[0, 1]
print (a[0, 1])
In [55]:
row_r1 = a[1, :] # Rank 1 view of the second row of a
row_r2 = a[1:2, :] # Rank 2 view of the second row of a
row_r3 = a[[1], :] # Rank 2 view of the second row of a
print (a)
print (row_r1, row_r1.shape)
print (row_r2, row_r2.shape)
print (row_r3, row_r3.shape)
In [56]:
e = np.arange(12)
print(e)
In [57]:
# f is a view of contents of e
f = e.reshape(3, 4)
print(f)
In [58]:
# Set values of e from index 5 onwards to 0
e[7:] = 0
print (e)
# f is also updated
print (f)
In [59]:
# We can get transpose of array by T attribute
print (f.T)
In [14]:
a = np.array([1, 2, 3])
print(np.concatenate([a, a, a]))
In [15]:
b = np.array([[1, 2, 3], [4, 5, 6]])
d = b / 2.0
# Use broadcasting when needed to do this automatically
print (np.vstack([a, b, d]))
In [16]:
# In machine learning, useful to enrich or
# add new/concatenate features with hstack
np.hstack([b, d])
print (np.concatenate([b, d], axis = 0))
In [63]:
x = np.array([[1,2],[3,4]], dtype=np.float64)
y = np.array([[5,6],[7,8]], dtype=np.float64)
In [64]:
# Elementwise sum; both produce the array
print (x + y)
print (np.add(x, y))
In [65]:
# Elementwise difference; both produce the array
print (x - y)
print (np.subtract(x, y))
In [66]:
# Elementwise product; both produce the array
print (x * y)
print (np.multiply(x, y))
In [67]:
# Elementwise division; both produce the array
# [[ 0.2 0.33333333]
# [ 0.42857143 0.5 ]]
print (x / y)
print (np.divide(x, y))
In [68]:
# Elementwise square root; produces the array
# [[ 1. 1.41421356]
# [ 1.73205081 2. ]]
print (np.sqrt(x))
In [69]:
# Multiply single number
print (x * 0.5)
In [70]:
a = np.array([1, 2, 3])
b = np.array([[1, 2, 3], [4, 5, 6]])
In [71]:
c = a + b
print(a.reshape(1, 3).shape, b.shape, c.shape)
print(c)
In [72]:
a.reshape((1, 1, 3)) + c.reshape((2, 1, 3))
Out[72]:
We can also get statistical results directly using sum, mean and std methods.
In [73]:
print (d)
print (d.sum())
print (d.sum(axis = 0))
print (d.mean())
print (d.mean(axis = 1))
print (d.std())
print (d.std(axis = 0))
We use the dot function to compute inner products of vectors, to multiply a vector by a matrix, and to multiply matrices. dot is available both as a function in the numpy module and as an instance method of array objects:
In [74]:
x = np.array([[1,2],[3,4]])
y = np.array([[5,6],[7,8]])
v = np.array([9,10])
w = np.array([11, 12])
# Inner product of vectors; both produce 219
print (v.dot(w))
print (np.dot(v, w))
In [75]:
# Matrix / vector product; both produce the rank 1 array [29 67]
print (x.dot(v))
print (np.dot(x, v))
In [76]:
# Matrix / matrix product; both produce the rank 2 array
# [[19 22]
# [43 50]]
print (x.dot(y))
print (np.dot(x, y))
In [77]:
x = np.matrix('1, 2, 3; 4, 5, 6')
y = np.matrix(np.ones((3, 4)))
print(x.shape)
print(y.shape)
print(x * y)
print(y.T * x.T)
You can find more in the document.
In [18]:
import pylab as plt
To make pylab work inside ipython:
In [19]:
%matplotlib inline
In [21]:
plt.plot([1,2,3,4], 'o-')
plt.ylabel('some numbers')
plt.show()
In [81]:
x = np.linspace(0,1,100);
y1 = x ** 2;
y2 = np.sin(x);
plt.plot(x, y1, 'r-', label="parabola");
plt.plot(x, y2, 'g-', label="sine");
plt.legend();
plt.xlabel("x axis");
plt.show()
In [82]:
# Create sample data, add some noise
x = np.random.uniform(1, 100, 1000)
y = np.log(x) + np.random.normal(0, .3, 1000)
plt.scatter(x, y)
plt.show()
In [83]:
# Compute the x and y coordinates for points on sine and cosine curves
x = np.arange(0, 3 * np.pi, 0.1)
y_sin = np.sin(x)
y_cos = np.cos(x)
# First plot
plt.subplot(2, 1, 1)
plt.plot(x, y_sin)
plt.title('Sine')
# Second plot
plt.subplot(2, 1, 2)
plt.plot(x, y_cos)
plt.title('Cosine')
# Show the figure.
plt.show()
In [84]:
mu, sigma = 100, 15
x = mu + sigma * np.random.randn(10000)
# the histogram of the data
n, bins, patches = plt.hist(x, 50, normed=1, facecolor='g', alpha=0.75)
plt.xlabel('Smarts')
plt.ylabel('Probability')
plt.title('Histogram of IQ')
plt.axis([40, 160, 0, 0.03])
plt.grid(True)
plt.show()
This is a common machine learning package with lots of algorithms, you can find detailed usage here.
Here is an example of KMeans cluster algorithm:
In [85]:
from sklearn.cluster import KMeans
In [86]:
mu1 = [5, 5]
mu2 = [0, 0]
cov1 = [[1, 0], [0, 1]]
cov2 = [[2, 1], [1, 3]]
x1 = np.random.multivariate_normal(mu1, cov1, 1000)
x2 = np.random.multivariate_normal(mu2, cov2, 1000)
print (x1.shape)
print (x2.shape)
plt.plot(x1[:, 0], x1[:, 1], 'r.')
plt.plot(x2[:, 0], x2[:, 1], 'b.')
plt.show()
In [87]:
x = np.vstack([x1, x2])
print (x.shape)
plt.plot(x[:, 0], x[:, 1], 'b.')
plt.show()
In [88]:
y_pred = KMeans(n_clusters=2).fit_predict(x)
x_pred1 = x[y_pred == 0, :]
x_pred2 = x[y_pred == 1, :]
print (x_pred1.shape)
print (x_pred2.shape)
plt.plot(x_pred1[:, 0], x_pred1[:, 1], 'b.')
plt.plot(x_pred2[:, 0], x_pred2[:, 1], 'r.')
plt.show()